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首页> 外文期刊>Journal of electrical and computer engineering >CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification
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CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification

机译：Chartec-net：文本分类的基于高效和基于轻量的字符的卷积网络

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摘要

This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems. This new architecture is composed of four building blocks for feature extraction. Each of these building blocks, except the last one, uses 1 x 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block. In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block. Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net's superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.
机译：本文介绍了一个极其轻量级的（刚刚超过两十万个参数）和计算的高效CNN架构，名为Chartec-net（基于字符的文本分类网络），用于基于字符的文本分类问题。这种新架构由四个构建块组成，用于特征提取。这些构建块中的每一个除外，使用1 x 1点朝向卷积层，为网络添加更多非线性，并增加每个构建块内的尺寸。另外，在每个构建块中使用快捷连接以促进网络上的渐变流，但更重要的是要确保在每个构建块上共享训练数据中存在的原始信号。八个标准大规模文本分类和情绪分析数据集的实验证明了Charec-Net对基线方法的卓越性能，与最先进的方法相比，竞争准确性，尽管Chartec-Net仅在181,427和225,323参数之间进行了参数和重量不到1兆字节。

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